| Sumario: | Quantitative proteomic analysis of microbial systems generates large datasets that can be difficult and time-consuming to interpret. Fortunately, many of the data display and gene-clustering tools developed to analyze large transcriptome microarray datasets are also applicable to proteomes. Plots of abundance ratio vs. total signal or spectral counts can highlight regions of random error and putative change. Displaying data in the physical order of the genes in the genome sequence can highlight potential operons. At a basic level of transcriptional organization, identifying operons can give insights into regulatory pathways as well as provide corroborating evidence for proteomic results. Classification and clustering algorithms can group proteins together by their abundance changes under different conditions, helping to identify interesting expression patterns, but often work poorly with noisy data such as typically generated in a large-scale proteomic analysis. Biological interpretation can be aided more directly by overlaying differential protein abundance data onto metabolic pathways, indicating pathways with altered activities. More broadly, ontology tools detect altered levels of protein abundance for different metabolic pathways, molecular functions, and cellular localizations. In practice, pathway analysis and ontology are limited by the level of database curation associated with the organism of interest.
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